HHMM-based Chinese lexical analyzer ICTCLAS

Zhang, Huaping; Huang, Yu; Xiong, Deyi; Liu, Qun

doi:10.3115/1119250.1119280

Cited by 358 publications

(180 citation statements)

References 3 publications

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“…the one-month People's Daily Corpus of 1998, and the first version of Penn Chinese Treebank [15]. We compared our approach against two state-of-the-art systems: one is based on a bi-gram word segmentation model [7], and the other based on a wordbased hidden Markov model [3]. For simplicity, we only considered three kinds of unknown words (personal name, location name, and organization name) in the all methods.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…Chooi-Ling Goh et al [16], Jianfeng Gao et al [8] and Huaping Zhang [3] adopted character information to handle unknown words; X. Luo [11], Yao Meng [12] and Shengfen Luo [17] each presented characterbased parsing models for Chinese parsing or new-word extraction. T. Nakagawa used word-level information and character-level information for word segmentation [6].…”

Section: Related Workmentioning

confidence: 99%

“…Chinese morphological analysis is a fundamental problem that has been studied extensively [1], [2], [3], [4], [5], [6], [7], [8]. Researchers make use of word or character features to cope with this problem.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Lexicon-Constrained Character Model for Chinese Morphological Analysis

Meng

Nishino

2005

Lecture Notes in Computer Science

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Abstract. This paper proposes a lexicon-constrained character model that combines both word and character features to solve complicated issues in Chinese morphological analysis. A Chinese character-based model constrained by a lexicon is built to acquire word building rules. Each character in a Chinese sentence is assigned a tag by the proposed model. The word segmentation and partof-speech tagging results are then generated based on the character tags. The proposed method solves such problems as unknown word identification, data sparseness, and estimation bias in an integrated, unified framework. Preliminary experiments indicate that the proposed method outperforms the best SIGHAN word segmentation systems in the open track on 3 out of the 4 test corpora. Additionally, our method can be conveniently integrated with any other Chinese morphological systems as a post-processing module leading to significant improvement in performance.

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Section: Experiments and Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Lexicon-Constrained Character Model for Chinese Morphological Analysis

Meng

Nishino

2005

Lecture Notes in Computer Science

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“…We utilized the free ICTCLAS [8] tool due to its mature performance, and the free NiuParser [9] tool due to its sophisticated models based on the statistic, such as Conditional Random Fields, Average Perceptron, Maximum Entropy, and Recurrent Neural Network.…”

Section: Methodsmentioning

confidence: 99%

Mining based on Extraction and Importance Evaluation Using Multi-Measures Methods for Electronic Documents

Xiong

Ding

2017

ITM Web Conf.

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Abstract-Mining the implicit knowledge in the electronic documents is a critical task in text analysis and data mining. To attain a knowledge-based view of the electronic documents, the clustering method based upon the topic cannot only be used, but also that based upon the extraction can be done. Therefore, a novel method for the clustering of the electronic documents, summarizing of the full text based on the extracted segments, and an evaluation using multi-measures for the importance to the document were presented. In the method, eighteen kinds of named entities and two kinds of syntactical phrases were extracted, and exploited for the text clustering. Then, a novel similarity equation was proposed for the calculation about the extractions. Meantime, three measures for the importance to the document were proposed, which provided a different view for the document's content, and recommended a prior checking for the users. Therefore, the method can improve the efficiency of the knowledge discovery, and enhance the management of the document on the large scale of document collection.

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“…ICT-CLAS [17] is adopted for segmentation and a stop word list including particle (e.g. , ), location words (e.g.…”

Section: Sentiment Featuresmentioning

confidence: 99%

Affect Computation of Chinese Short Text

Mao

Lin

Xue

2012

IEICE Trans. Inf. & Syst.

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SUMMARYMicroblogs are a rising social network with distinguishing features such as simplicity and convenience and has already attracted a large number of users and triggered massive information explosion concerning individuals' own statuses and opinions. While sentiment analysis of the messages in microblogs is of great value, most of present studies are on English microblogs and few are on Chinese microblogs. Compared to English, Chinese has its unique expression style, such as no spaces or other word delimiters. Furthermore, Chinese short text also has its own properties. Thus we are inspired to explore effective features for sentiment classification of Chinese short text. In this paper, we propose to study userrelated sentiment classification of Chinese microblogs in terms of the statistical and semantic characteristics, and deisgn the corresponding features: ratio of positive words and negative words (PNR), position feature (POS ), collocation of verbs (COL), auxiliary words (AU). Then we employ an SVM-based method to classify the sentiment. Experiments show that the features we design is effective in recognizing the sentiment of messages in microblogs.

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HHMM-based Chinese lexical analyzer ICTCLAS

Cited by 358 publications

References 3 publications

A Lexicon-Constrained Character Model for Chinese Morphological Analysis

A Lexicon-Constrained Character Model for Chinese Morphological Analysis

Mining based on Extraction and Importance Evaluation Using Multi-Measures Methods for Electronic Documents

Affect Computation of Chinese Short Text

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